An empirical investigation of forward-looking retailer performance using parking lot traffic data derived from satellite imagery

Feng, Cong; Fay, Scott

doi:10.1016/j.jretai.2022.03.004

Cited by 9 publications

(14 citation statements)

References 82 publications

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“…Recent studies have applied remote sensing data in management literature. For instance, Feng and Fay (2022) and Katona et al (2018) used parking lot traffic to predict forwardlooking retailer performance. They found a positive relationship between parking lot traffic and forward-looking retailer performance.…”

Section: Methodsmentioning

confidence: 99%

“…and channel member selection Verhetsel, 2005;Maxham et al, 2008;Liu et al, 2016;Mani et al, 2015;Gauri et al, 2017;Katona et al, 2018;Tian et al, 2021;Feng and Fay, 2022), we examine whether a positive correlation exists between parked cars and sales performance. While large companies use reports of the number of vehicles in their parking lots as a crucial variable in developing models to predict store earnings over a given period (Cisek et al, 2017), academia has not paid much attention to the subject.…”

Section: Satellite Imagesmentioning

confidence: 99%

“…Remote sensing has been applied to business-related activity in very few studies. To our knowledge, only Katona et al (2018) and Feng and Fay (2022) have used parking lot occupancy to estimate sales-related phenomena. Katona et al (2018) focussed on the stock market performance of companies with access to satellite data.…”

Section: Satellite Imagesmentioning

confidence: 99%

“…Their paper shows a correlational relationship between parking traffic and forward-looking sales before moving to their primary goal. Feng and Fay's (2022) work uses a more complex econometric model that focusses on the causal relationship between parking traffic and sales performance in time, both on firm and industry levels. Our approach builds upon both works, but we do not aim to predict sales over time.…”

Section: Satellite Imagesmentioning

confidence: 99%

“…To address the challenges associated with collecting, processing and utilising internal and external data, as well as to enhance existing approaches for measuring channel member performance, this work aims to propose an open-source method for measuring and predicting channel performance using machine learning (ML) algorithms applied to freely available remote sensing data, specifically satellite imagery. Feng and Fay (2022) have used this type of data to forecast the sales of a single retailer, demonstrating that the measure (car count) improves the model's performance and helps predict future sales. Building on their work, we propose a method in which manufacturers can utilise parking lot traffic data from satellite imagery for strategic planning tasks.…”

Section: Introductionmentioning

confidence: 99%

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Satellite imagery and machine learning for channel member selection

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PurposeThis study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.Design/methodology/approachThe authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.FindingsThe relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.Research limitations/implicationsThe proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.Originality/valueThis research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.

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Section: Methodsmentioning

confidence: 99%